Multi-user slice resource allocation method based on competitive game

ABSTRACT

The present disclosure provides a multi-user slice resource allocation method based on competitive game. In the method, first model a system as a two-tier architecture of virtual infrastructure service providers (VInPs) and users, and build a VInP utility model and a user utility model; then divide slice resource allocation into nodes and links, and build a node power consumption model and a link power consumption model, then determine a revenue of the VInP and a revenue of a user; and calculate a total revenue of a slice according to the revenue of the VInP and the revenue of the user, and use the total revenue of the slice as a network model; then solve the network model, where the VInP is used as a seller, the user is used as a buyer, the seller determines an initial price according to a total quantity of slice resources, and the buyer bids on the slice, and allocate the slice resources by using a competitive game mechanism. The method of the present disclosure may enhance the utility of the user, and improve a resource allocation effect.

CROSS REFERENCE TO RELATED APPLICATION

This patent application is a national stage application of InternationalApplication No. PCT/CN2021/100188, filed on Jun. 15, 2021, which claimspriority to the Chinese Patent Application No. 202010543434.1, filedwith the China National Intellectual Property Administration (CNIPA) onJun. 15, 2020, and entitled “MULTI-USER SLICE RESOURCE ALLOCATION METHODBASED ON COMPETITIVE GAME”. Both of the aforementioned applications areincorporated by reference herein in their entireties for all purposes.

TECHNICAL FIELD

The present disclosure relates to the technical field of computer, andin particular to a multi-user slice resource allocation method based oncompetitive game.

BACKGROUND ART

With the development of science and technology, great changes have takenplace in people's production and lifestyle, and especially thedevelopment of communication technology has promoted earth-shakingchanges in human society. Computer networks reduce people's geographicalrestrictions infinitely, allow many industries to be shifted fromoffline to online, such as online courses, video conferences, smarthealthcare, industrial Internet, and the like, thereby promoting thedevelopment of all walks of life. In the future, with the development ofcommunication theory and technology, computer networks will promote thesocial development and improve people's lives in an unprecedentedmanner.

At the beginning of the rise of networks, wired networks were used fordata transmission all the time. Wired networks have relatively highstability, reliability, and data transmission rate, and thus are widelyused in military and commercial fields. However, defects such as highinfrastructure deployment costs, difficult maintenance, low resourceutilization, and geographical restrictions of wired networks are alsogradually emerging. In recent years, due to the development of wirelessnetwork technology, wireless networks are becoming more and morepopular, breaking the geographical restrictions of the traditional wirednetworks and becoming a mainstream direction of current networkdevelopment. From 1G and 2G that only support voice and text services to3G and 4G that have been applied commercially in supporting videoservices, an amount of data transmitted over wireless networks has shownan explosive growth, and it has been more and more difficult for theexisting mobile communication systems to meet people's demands.Therefore, a commercial application of the 5G network technology hasbeen put on the agenda. The 5G networks have put forward higherrequirements for various performances of data transmission, includinglow latency, high bandwidth, high reliability, and the like. The networkslicing, as a key technology of 5G networks, is significant to studymanagement and allocation of network slice resources, while the cloudradio access network (C-RAN), as a new wireless network accessarchitecture, has been a key architecture of 5G networks due to itsfeatures such as strong scalability, centralized deployment ofresources, and low latency. Therefore, with limited resources,management and allocation of network slice resources and provision ofefficient network services have important practical and guidingsignificance, which are mainly reflected in the following aspects.

(1) Various emerging industries have put forward increasingly higherrequirements for network performances, including low latency, highbandwidth, high reliability, and the like. The traditional best-effortmodel has been unable to meet the demands of users, and it issignificant for users to strengthen the management of network resources.

(2) Due to the increase in application types and the rapid growth in thenumber of users, network operators try to meet the demands of users byincreasing the deployment of base stations, however, which has led tothe resource utilization reduced. Strengthening the management ofnetwork resources can reduce the network costs and improve the resourceutilization.

(3) A dense network deployment architecture causes serious inter-cellinterference, and strengthening management of network resources to avoidor reduce inter-cell signal interference has been a hot spot of currentresearch.

(4) In view of scarce spectrum resources, sharing spectrum resources issignificant to the management of network resources.

The C-RAN architecture is a new network architecture on the accessnetwork side, in which some BaseBand Units (BBU) are centralized to forma BaseBand Unit pool (BBU Pool), and then are subjected to a unifiedmanagement and allocation. “C” in C-RAN mainly has four meanings:centralized, cooperative radio, real-time cloud infrastructure, andclean. The C-RAN may effectively reduce the costs of device deployment,realize the dynamic scheduling of network resources, and provide alow-cost, easy-to-extend, high-bandwidth wireless network architecture,which has broad significance in both application and research fields.The C-RAN has the following advantages:

(1) The C-RAN is an environment-friendly, clean and energy-savingnetwork infrastructure. Through centralized processing on BBU, the C-RANgreatly reduces a number of base stations, network construction costs,and the electricity consumption of site-supported devices, therebyreducing network energy consumption. The development of cooperativeradio technology has effectively solved the problem of interferenceamong Remote Radio Heads (RRHs), greatly increasing the deploymentdensity of RRHs, and reducing the energy consumption of datatransmission.

(2) The C-RAN has improved a capacity of a network. Centralizedmanagement of the BBUs in C-RAN forms the BBU pool, and the virtual basestations may share the signals and traffic of all users in the network,thereby improving the utilization efficiency of a spectrum resource.

(3) The C-RAN has improved a load balancing capacity of the network. TheC-RAN may adaptively handle the non-uniform traffic of users. A BBUresource pool performs centralized processing on traffic within acoverage area thereof, which can meet the non-uniform traffic demands ofusers.

(4) Millimeter-wave band technology has gained development in the C-RAN.A corresponding spectrum is required for guaranteeing a high networkcapacity and a high data transmission rate in the 5G slice network. Dueto the lack of spectrum resources in low frequency bands, new spectrumresources need to be developed to meet the demands of users. Themillimeter wave technology can integrate MIMO technology to achieve themulti-carrier constraint, thereby greatly improving the utilizationefficiency of spectrum.

In the process of implementing the present disclosure, the inventor ofthis application found that the C-RAN architecture has great advantagesover the traditional network architectures. However, there are somechallenges in management and allocation of network slice resources underthe C-RAN architecture, which are mainly manifested in the followingaspects:

There is a specific relationship between the estimated value of anetwork slice given by a user and the value of the network slice itself,which is difficult to be reflected in the existing model. This imposes ahigher challenge for users in reasonably allocating network sliceresources. In addition, the resource management and allocation ofnetwork slicing are often directed to single-user multi-operatorsituations, with a little study of multi-user multi-operator scenarios.

It can be seen that the method in the prior art has a technical problemthat it is difficult to guarantee an effect for multi-user resourceallocation.

SUMMARY

An object of the present disclosure is to provide a multi-user sliceresource allocation method based on competitive game, so as to solve orat least partially solve the technical problem of a poor allocationeffect in the prior art.

To solve the foregoing technical problems, the present disclosurediscloses a multi-user slice resource allocation method based oncompetitive game, including:

S1: modeling a system as a two-tier architecture of virtualinfrastructure service providers (VInPs) and users, where a VInP layercomprises a plurality of VInPs, and a user layer comprises a pluralityof users;

S2: building a VInP utility model and a user utility model;

S3: dividing the slice resource allocation into nodes and links, andbuilding a node power consumption model and a link power consumptionmodel;

S4: determining a revenue of the VInP according to the VInP utilitymodel, the node power consumption model, and the link power consumptionmodel, and determining a revenue of a user according to the user utilitymodel, the node power consumption model, and the link power consumptionmodel;

S5: calculating a total revenue of a slice according to the revenues ofthe VInP and the user, and using the total revenue of the slice as anetwork model; and

S6: solving the network model, where the VInP is used as a seller, theuser is used as a buyer, the seller determines an initial priceaccording to a total quantity of slice resources, and the buyer bids onthe slice, and allocating the slice resources by using a competitivegame mechanism.

In some implementations, building a VInP utility model in S2 is:

G(p,q)=pq−cq,  (1)

where p represents an initial unit price of the slice resources given bythe VInP, q represents a quantity of slice resources allocated by theVInP, and c represents a cost unit price of the slice resources; theuser utility model is:

F(p,q)=u(q)−l(p,q)+v(q),  (2)

where u(q) represents the utility generated from the slice resourcesacquired by a user, l(p,q) represents a cost expended by the user forthe resource, v(q) represents the user satisfaction, u(q)=wln(1+q), l(p,q)=pq,

${{v(q)} = {\ln\left( \frac{m + q}{m} \right)}},$

where w is a constant greater than 0 and represents a user weight; wherem represents a quantity of resources requested by the user.

In some implementations, building a node power consumption model in S3specifically includes:

calculating the power consumption of a single node:

P _(i) =P _(i) ^(SE) +P _(i) ^(RE),  (3)

where P represents the link power consumption of a accessed node i in aslice, P_(i) ^(SE) represents the transmission power consumption of theaccessed node i, and P_(i) ^(RE) represents the reception powerconsumption of the accessed node i. Calculating the node powerconsumption of the slice according to the power consumption of thesingle node:

$\begin{matrix}{{p_{i}^{s} = {\sum\limits_{l \in \Theta}{h_{i,l}^{s}p_{i}}}},} & (4)\end{matrix}$

where h_(i,l) ^(s) represents whether the node i is used in a path l,the path represents a complete link from a source node to a destinationnode, and s represents a label of the slice; determining a node priceρ_(i)(p_(i) ^(s)) according to the node power consumption of the slice,where the node price is a function of the node power consumption, andρ_(i)(p_(i) ^(s)) is used as the node power consumption model.

In some implementations, building a link power consumption model in S3specifically includes:

calculating a bandwidth of a link e:

$\begin{matrix}{{x_{e}^{s} = {{\sum\limits_{l \in \Psi}y_{l}^{s}} = {\sum\limits_{l \in \Theta}{g_{e,l}^{s}y_{l}^{s}}}}},} & (5)\end{matrix}$

where a network controller calculates L_(s) candidate paths from thesource node to the destination node that meet user demands. The pathsfrom the source node to the destination node are denoted by Ψ and amountto O paths in total. The candidate paths denoted by Θ are included inall paths from the source node to the destination node, namely, Θ⊆Ψ,Ψ={l₁, l₂, . . . , l_(L) _(s) , . . . , l_(O) _(f) }. y_(l) ^(s)represents bandwidth allocation on a path l, and g_(e,l) ^(s) representswhether a link e is used in the path l of a slice s. The methods forcalculating the candidate paths includes: 1) by using a primal-dualalgorithm starting from any feasible flow in a network with a flow value(also known as available bandwidth) x≤v, increasing the flow values oflink in the network and modify potentials of nodes; 2) iterating thelinks and the nodes in the network until a flow that meets apredetermined constraint condition is obtained, to obtain a targetcandidate path, namely, bandwidth allocation of the link. Herein vrepresents a flow value requested by the user, namely, a requested datatransmission rate. If an initial flow value is greater than v, thetarget candidate path is directly obtained. A link price ρ_(e)(x_(e)^(s)) is calculated according to the bandwidth of the link e, which is afunction of the link bandwidth and is used as the link power consumptionmodel.

In some implementations, determining a revenue of the VInP according tothe VInP utility model, the node power consumption model, and the linkpower consumption model in S4 includes:

determining the utility obtained by the VInP for a slice s:

$\begin{matrix}{{{Q_{p}^{s}\left( {x^{s},p^{s}} \right)} = {{{\phi_{s}\left( {x^{s},p^{s},\rho} \right)}r} - {\left( {{\sum\limits_{l \in \Theta}{{\varphi_{i}\left( p_{i}^{s} \right)}h_{i,l}^{s}}} + {\sum\limits_{l \in \Theta}{{\varphi_{e}\left( x_{e}^{s} \right)}g_{e,l}^{s}}}} \right)r}}},} & (6)\end{matrix}$

where ϕ_(s)(x^(s),p^(s),ρ)r represents a charge for the slice s providedby the VInP and,

$\left( {{\sum\limits_{l \in \Theta}{{\varphi_{i}\left( p_{i}^{s} \right)}h_{i,l}^{s}}} + {\sum\limits_{l \in \Theta}{{\varphi_{e}\left( x_{e}^{s} \right)}g_{e,l}^{s}}}} \right)r$

represents a cost for providing a service. φ_(i)(p_(i)) represents theprice of node i and φ_(e)(x_(e) ^(s)) represents the price of link e.φ(·) is a monotonically increasing function. h_(i,l) ^(s) representswhether the node i is used in path l and g_(e,l) ^(s) represents whetherthe link e is used in path l. The revenue of the VInP is determined bythe utility obtained by the VInP for the slice s:

Q _(P)=Σ_(s∈S) Q _(p) ^(s)(x ^(s) ,p ^(s)),  (7)

where a server has the largest profit, i.e., the following conditionsare met:

max Q _(p) ^(s)(x ^(s) ,p ^(s)),

s.t.:x _(e) ^(s) ≤c _(e) ^(pro),

p _(i) ^(s) ≤v _(i) ^(pro),

where x_(e) ^(s) represents the bandwidth of link e and c_(e) ^(pro)represents the remaining maximum bandwidth available for an allocationprovided by the link e. p_(i) ^(s) represents the power consumption ofnode i in the slice s, and v_(i) ^(pro) represents the remaining maximumdata transmission rate provided and supported by the node i.

In some implementations, determining a revenue of a user according tothe user utility model, the node power consumption model, and the linkpower consumption model in S4 includes:

determining the utility of each user:

U _(s)(r)=w _(s) log(l+r),  (8)

where w_(s) represents the service quality request level of the user,and r represents the data transmission rate;

determining the cost of the user for building the slice according to thenode power consumption model and the link power consumption model:

$\begin{matrix}{{{\phi_{s}\left( {x^{s},p^{s},\rho} \right)} = {{\sum\limits_{e \in \xi_{s}}{\rho_{e}\left( x_{e}^{s} \right)}} + {\sum\limits_{i \in N_{s}}{\rho_{i}\left( x_{i}^{s} \right)}}}},} & (9)\end{matrix}$

where x_(e) ^(s) represents a link allocation and p_(l) ^(s) representsa node allocation status, ρ_(e)(.) represents the functionalrelationship between a link unit price and a link allocation bandwidth,ρ_(i)(.) represents the relationship between a node unit price and thenode power consumption,

$\sum\limits_{e \in \xi_{s}}{\rho_{e}\left( x_{e}^{s} \right)}$

represents a cost paid by the user for purchasing the link,

$\sum\limits_{i \in N_{s}}{\rho_{i}\left( x_{i}^{s} \right)}$

represents a cost paid by the user for purchasing the node;

determining the revenue of the user according to the utility and cost ofthe user for building the slice, namely, the total revenue of the userfor purchasing all slices:

Q _(c)=Σ_(s∈S) Q _(c) ^(s),  (10)

where the expression above is maximized:

${{\max Q_{c}^{s}} = {{\sum\limits_{f \in K_{s}}{U_{s}\left( r_{f} \right)}} - {{\phi_{s}\left( {x^{s},p^{s},\rho} \right)}r}}},$s.t.:x _(e) ^(s) ≥c _(e) ^(req) ,p _(i) ^(s) ≥v _(i) ^(req),

where Q_(c) ^(s) represents the revenue from the slice s purchased bythe user, S represents a set of slices, r_(f) represents the datatransmission rate requested by a user f, the expression following s.t.represents the constraint condition, x_(e) ^(s) represents the bandwidthof link e, c_(e) ^(req) represents the bandwidth requested by the user,p_(i) ^(s) represents the power consumption of node i in the slice s,and v_(i) ^(req) represents the node data transmission rate requested bythe user.

In some implementations, S5 specifically includes:

calculating the total revenue of the slice according to the revenue ofthe VInP and the revenue of the user:

$\begin{matrix}{{\max Q^{s}} = {\max\left( {Q_{c}^{s} + Q_{p}^{s}} \right)}} & (11)\end{matrix}$${= {\max\left( {{\sum\limits_{f \in K_{s}}{w_{s}\log\left( {1 + {\sum\limits_{l \in \Theta}{q_{f,l}^{s}y_{l}^{s}}}} \right)}} - {\left( {{\sum\limits_{l \in \Theta}{{\varphi_{i}\left( p_{i}^{s} \right)}h_{i,j}^{s}}} + {\sum\limits_{l \in \Theta}{{\varphi_{e}\left( x_{e}^{s} \right)}g_{e,l}^{s}}}} \right)r}} \right)}},$s.t. : x_(e)^(s) ≥ c_(e)^(req), p_(i)^(s) ≥ v_(i)^(req), x_(e)^(s) ≤ c_(e)^(pro), p_(i)^(s) ≤ v_(i)^(pro),

where the constraint condition consists of the constraint on the userrequesting a node or a link, and the constraint on a VInP end respondingto a node or bandwidth constraint.

In some implementations, when the competitive game mechanism is used toallocate the slice resources in S6, a mutual information-basedcompetitive game strategy is adopted, and the revenue of the user isused as an estimated value of the user for the slice, the revenue of theVInP is used as an estimated value for the slice, an estimated value aof the user for the slice is used as a random variable, a value of theslice is also a random variable p, there is a specific relationshipbetween a and p, and I(a,p) represents a degree of correlation betweenthe estimated value for the slice and the value of the slice.

The foregoing one or more technical solutions in the embodiments of thisapplication have at least one or more of the following technicaleffects.

Aiming at a multi-user network slice resource allocation method, thepresent disclosure models the network slice resource allocation as atwo-tier architecture being composed of a virtual infrastructure serviceprovider (VInP) and a user, and the VInP and the user establish theutility models thereof. A marginal benefit of economics is used to builda revenue model for the user, and the user satisfaction is modeled byconsidering the relationship between the resources requested by the userand the actual allocated resources. A cooperative competition mechanismis used to adjust the resource price and the amount of allocatedresources, such that a Nash equilibrium is finally reached. Compared toother game strategies, the utility of the user has been improved; for asituation where a plurality of users request the same network sliceresource, the node energy consumption and the link selection play animportant role in the slice resource allocation. The slice is modeled asa combination of nodes and links, the plurality of users bid on theslice, and the competitive game strategy is used to allocate the sliceresources to the users to improve the allocation effect of the sliceresources.

Furthermore, it is proposed to use the mutual information to reflect therelationship between an estimated value and a real value, and a sliceresource allocation scheme is obtained by adjusting a bidding strategy.Finally, the utilization rate of node and link resources in the schemeis proved to be improved through experiments, and the introduction ofmutual information enables the system to reach a Nash equilibriumearlier and improves its network benefits.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be explained in detail with reference to theaccompanying drawings.

FIG. 1 is an overall flowchart of a multi-user slice resource allocationmethod based on competitive game according to the present disclosure;

FIG. 2 is a model frame diagram of an auction system in a competitivegame process according to the present disclosure;

FIG. 3 is a graph of node utilization rate obtained by three methods;

FIG. 4 is a graph of link utilization rate obtained by three methods;and

FIG. 5 is a graph of VInP benefits obtained by three methods.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The present disclosure proposes a multi-user slice resource allocationmethod based on competitive game theory, which may be used formulti-user slice resource allocation and improves an allocation effect.

In order to achieve the foregoing objectives, the present disclosureemploys the following technical solutions:

1. Research on a multi-user multi-VInP scenario.

A user satisfaction-based network slice resource allocation andmanagement method under a C-RAN architecture is proposed. The networkslice resource allocation is modeled as a two-tier architectureconsisting of VInPs and users. While building a user utility model, amanner for quantizing user satisfaction on allocated resources isproposed to maximize a utility function of users.

2. Considering the influences of nodes and links on networks, a mutualinformation-based network slice resource allocation and management modelunder a C-RAN architecture is built. Based on the relationship between avalue of a slice estimated by a user who competes for network sliceresources and a value of a slice, a mutual information-based networkslice resource management scheme is proposed.

In order to make the objectives, technical solutions and advantages ofembodiments of the present disclosure more clear, the technicalsolutions in the embodiments of the present disclosure will be clearlyand completely described below in conjunction with the accompanyingdrawings in the embodiments of the present disclosure. Apparently, theembodiments as described are some, instead of all the embodiments of thepresent disclosure. All other examples obtained by a person of ordinaryskill in the art based on the embodiments of the present disclosurewithout creative efforts shall fall within the protection scope of thepresent disclosure.

This embodiment provides a multi-user slice resource allocation methodbased on a competitive game, and the method includes steps S1 to S6.

In step S1, a system is modeled as a two-tier architecture of virtualinfrastructure service providers (VInPs) and users, where a VInP layercomprises a plurality of VInPs and a user layer comprises a plurality ofusers.

In step S2, a VInP utility model and a user utility model are built.

In step S3, slice resources are divided into the nodes and links forallocation, and a node power consumption model and a link powerconsumption model are built.

In step S4, the revenue of each VInP is determined according to the VInPutility model, the node power consumption model, and the link powerconsumption model, and the revenue of each user is determined accordingto the user utility model, the node power consumption model, and thelink power consumption model.

In step S5, the total revenue of a slice is calculated according to therevenue of the VInP and the revenue of the user, and the total revenueof the slice is used as a network model.

In step S6, the network model is solved, where the VInP is used as aseller, the user is used as a buyer, the seller determines an initialprice according to a total quantity of slice resources, and the buyerbids on slices, and the slice resources are allocated by using acompetitive game mechanism.

Specifically, network resources are sliced under the C-RAN architecture,a physical network is mapped into a virtual network with specificfunctions, and spectrum resources of network slices are allocated sothat a benefit of the network is maximized. The system is modeled as atwo-tier architecture composed of multiple virtual infrastructureservice providers (VInPs) and multiple users. As a limited resourcepool, a VInP is an owner of network slices. The VInP makes a pricingstrategy based on market information with a goal of maximizing its ownbenefit. A user applies for a specific quantity of resources to an upperlayer according to a network status and its own needs, so as to meet itsown needs. The VInP layer wishes to use the limited resources to obtaina maximum revenue, while a lower user layer wishes to obtain a maximumquantity of resources at the lowest cost. Therefore, the upper and lowerlayers experience a game process, and finally the system reaches a Nashequilibrium.

Referring to FIG. 1 , FIG. 1 is an overall flowchart of a multi-userslice resource allocation method based on a competitive game. As shownin FIG. 1 , at the beginning stage, for users and VInPs, their utilitymodels are established respectively. There is an initial price for anynetwork slice resources, namely, the more a total quantity of thenetwork resources is, the lower the initial price is. Then node powerconsumption and link power consumption are modeled to obtain networkslices composed of nodes and links, and finally, the node model and thelink model are solved to obtain a quantity of network slices requestedby a user, namely, the quantity of resources. A final network sliceresource allocation scheme is solved by the network model (also called“an auctioneer”).

In an embodiment, a VInP utility model in step S2 is built as follows:

G(p,q)=pq−cq,  (1)

where p represents an initial unit price of slice resources given by theVInP, q represents a quantity of slice resources allocated by the VInP,and c represents a unit cost of the slice resources.

A user utility model is expressed as:

F(p,q)=u(q)−l(p,q)+v(q),  (2)

where u(q) represents a utility generated from the slice resourcesacquired by a user. l(p,q) represents a cost expended by the user forresources. v(q) represents the user satisfaction. u(q)=wln(1+q),l(p,q)=pq, where w represents a user weight and is a constant greaterthan

${{0.{v(q)}} = {\ln\left( \frac{m + q}{m} \right)}},$

wherein m represents a quantity of resources requested by the user.

Specifically, for a slice s (s∈S), it is supposed that there are a totalof n users and m VInPs. The quantity of resources requested by thei^(th) user is mi, which is an ideal resource allocation quantity. Thequantity of allocated resources provided by the j^(th) VInP is q_(j),and q_(j)<m_(i). Generally, the quantity of resources requested by auser is always greater than the quantity of resources allocated by aVInP. The total quantity of resources that the j^(th) VInP can allocateto a user is Q_(j), and q_(j)<Q_(j), that is to say, the quantity ofallocated resources is less than the total quantity of resources. Aresource unit price provided by the j^(th) VInP is p_(j), and theresource unit price is related to the total quantity of resources, whichmay be expressed as:

$\begin{matrix}{{p_{j} = \frac{b}{Q_{j}}},} & (12)\end{matrix}$

where b is a constant, and when a user selects a VInP for trading, theresource unit price and the quantity of resources need to be considered.The lower the resource unit price is, the higher the user utility is.The smaller the difference between the quantity of requested resourcesand the quantity of resources that can be allocated by the VInP is, andthe higher the user satisfaction is. Therefore, a VInP selected by thei^(th) user for trading may be expressed as:

$\begin{matrix}\frac{{j = {\arg\min\left( {\left( {m_{i} - q_{j}} \right) \times \frac{b}{Q_{j}}} \right)}},}{j} & (13)\end{matrix}$

then the optimal price strategy is obtained through collaborativecompetition with the j^(th) VInP, namely, the optimal price of slice sfor user i is obtained.

For the convenience of research, a price cooperative competitionscenario between the j^(th) VInP and the i^(th) user is discussed below.In the process of cooperative competition, the resource price and thequantity of allocated resources are changed, and a Nash equilibrium isreached finally. The initial unit price of resources given by VInP is p,and the quantity of resources allocated by VInP is q. The quantity ofresources requested by user is m, and q<m. The initial price is relatedto the total quantity of resources. The larger the total quantity ofresources is, the lower the initial price is, i.e.:

$\begin{matrix}{{p = \frac{b}{Q}},} & (14)\end{matrix}$

where b is a constant greater than 0.

Therefore, the utility model of VInP reflects the difference between aresource price and a resource cost, as shown in Eq. (1).

Considering the influence of user satisfaction on utility function, theutility function of a user includes three parts: the utility generatedfrom the acquired resources, the resource expenditure cost, and the usersatisfaction. The utility generated from the acquired resources isexpressed by u(x), representing the revenue brought by allocating xresources. The present disclosure adopts a conservative utilityfunction, namely, a logarithmic function, as the utility function ofresources, which is expressed as:

u(q)=w ln(l+q),  (15)

where w is a constant greater than 0, representing a user weight. Themeaning of utility function is that the specific quantity of resourcesallocated to a user can basically meet the demands of the user, but whenthe quantity of allocated resources is large enough, the growth rate ofuser revenue may decrease with the increase of the allocated resources.The resource expenditure cost of user is the product of resource unitprice and the quantity of allocated resources, which may be expressedas:

l(p,q)=pq.  (16)

User satisfaction is the perceptual knowledge of user for a resourceallocation scheme. The present disclosure quantifies user satisfaction,and it may be expressed by the quantity of resources requested by a userand the quantity of resources allocated by VInP to the user:

$\begin{matrix}{{v(q)} = {\ln{\left( \frac{m + q}{m} \right).}}} & (17)\end{matrix}$

As the quantity of allocated resources increases, user experience willbe better, and user satisfaction will increase. When the quantity ofallocated resources decreases, user satisfaction also graduallydecreases, and when the quantity of resources allocated to a user is 0,the user satisfaction is also 0. Therefore, the total revenue of theresources requested by a user is F(p,q), which may be expressed in theform of Eq. (2).

For a system model, it is necessary to build utility models for usersand VInPs respectively, and a system utility model is obtained byanalyzing the utility models built for users and VInPs respectively. Theuser adjusts a quantity of purchased resources according to a pricestrategy of the VInP to optimize a utility function thereof, while theVInP adjusts a resource price according to the quantity of resourcespurchased by the user to optimize a resource utility function thereof.And this process is repeated to finally reach a Nash equilibrium. Anextremely high price set by the VInP may cause excessive resources to beidle, resulting in a waste of resources, while an extremely low pricemay cause a very large load for an entire network, affecting the userexperience performance. Therefore, in the game process, it is desirableto formulate a reasonable price and quantity of purchased resources,such that both VInP and user can meet demands thereof.

In the implementation, building a node power consumption model in stepS3 specifically includes:

calculating a power consumption of a single node:

P _(i) =P _(i) ^(SE) +P _(i) ^(RE),  (3)

where P_(i) represents the link power consumption of an accessed node iin a slice, P_(i) ^(SE) represents the transmission power consumption ofthe accessed node i, and P_(i) ^(SE) represents the reception powerconsumption of the accessed node i;

calculating the power consumption of all nodes in one slice according tothe power consumption of single node:

p _(i) ^(s) =Σh _(i,l) ^(s) p _(i),  (4)

where h_(i,l) ^(s) represents whether a path l passes through node i,the path represents a complete link from a source node to a destinationnode, and s represents the label of a slice; one slice may include aplurality of nodes, and Eq. (4) represents total node power consumptionin one slice, which is a sum of the power consumption of a plurality ofnodes;

determining a node price ρ_(i)(p_(i) ^(s)) according to total node powerconsumption of slice s, where the node price is a function of total nodepower consumption, and ρ_(i)(p_(i) ^(s)) is used as a node powerconsumption model.

Specifically, for network slice resources, the node energy consumptionand selection of links play a leading role in slice resource allocation.Nodes and links are used as the underlying infrastructure of a network,and the node energy consumption and selection of links is of greatsignificance for building a network slice that meets demands of user. Toallocate network slice resources under the C-RAN architecture, bothnodes and links are modeled. A network controller manages an underlyingnetwork through network function virtualization (NFV) and softwaredefined networking (SDN), maps a physical network to a virtual network,and forms a network slice with specific functions, which may berepresented by an ordered combination of nodes and links. Various slicesare logically isolated from each other, and physically share theunderlying infrastructure. The underlying network may be expressed as adirected graph G(N,ξ), where N={i|i=1, 2, . . . , N} represents a set ofnodes, and ξ={e|e=1, 2, . . . L} represents a set of links. For arequest of user, slices are established based on a slice matchingprinciple. A set of slices may be expressed as S={s|s=1, 2, . . . n},where n represents a number of slices. One slice may meet the demands ofa single user or a plurality of users. Therefore, a plurality of usersmay compete for a same slice resource.

The energy consumption of each node plays an important role inestablishing a slice. The selection of nodes not only needs to meet aservice quality request of user but also needs to minimize the powerconsumption of the network system. According to Shannon formula, atransmission rate of node i may be expressed as:

$\begin{matrix}{{u_{i} = {B_{i}\log_{2}\left( {l + \frac{p_{i}^{se}h_{i}}{\sigma^{2}}} \right)}},} & (18)\end{matrix}$

where p_(i) ^(se) represents the data transmission rate of node, B_(i)represents the bandwidth allocated to node i, h_(i) represents thechannel gain of node i, and σ² represents a channel noise power.Therefore, the selection of candidate nodes needs to meet p_(i)^(se)≥p_(i) ^(req), and p_(i) ^(req) represents the transmission raterequested by user.

Overall power consumption of system is mainly divided into two parts:the power consumed by node to send data, and the power consumed by nodeto receive data. When the i^(th) node is accessed, 1≤i≤n, and the powerconsumption of a single node may be modeled as the form of Eq. (3).

The transmission power consumption p_(i) ^(SE) of a node may be derivedfrom the Shannon formula as follows:

$\begin{matrix}{p_{i}^{SE} = {\frac{\sigma^{2}}{h_{i}}{\left( {2^{\frac{u_{i}}{B_{i}}} - 1} \right).}}} & (19)\end{matrix}$

At a receiving node i, filtering, shaping, amplifying, analog-to-digitalconversion, and the like need to be performed on a signal, andtherefore, receiving power consumption of node i may be expressed as:

p _(i) ^(RE) =M _(i)(p _(i) ^(LNA) +p _(i) ^(MIX) +p _(i) ^(IFA) +p _(i)^(F) +p _(i) ^(AD),  (20)

where M_(i) represents a number of antennas on the i^(th) node, p_(i)^(LNA) represents the power consumption of a low-noise amplifier on thei^(th) node, p_(i) ^(MIX) the power consumption of a mixer on the i^(th)node, p_(i) ^(IFA) represents the power consumption of a frequencyamplifier on the i^(th) node, p_(i) ^(F) represents the powerconsumption of a filter on the i^(th) node, and p_(i) ^(AD) representsthe power consumption for converting an analog signal into a digitalsignal on the i^(th) node.

By modeling the network energy consumption, when selecting nodes, it isnot only necessary to meet the service quality request of user but alsonecessary to ensure that the node energy consumption is minimized. Forthe network slice resource allocation, not only the nodes but also thelinks need to be managed, and the links need to be planned to improvethe performance of system.

In the implementation, building a link power consumption model in stepS3 specifically includes:

calculating a bandwidth of link e:

$\begin{matrix}{{x_{e}^{s} = {{\sum\limits_{l \in \Psi}y_{l}^{s}} = {\sum\limits_{l \in \Theta}{g_{e,l}^{s}y_{l}^{s}}}}},} & (5)\end{matrix}$

where a network controller calculates L_(s) candidate paths from asource node to a destination node that meet the user demands. There areO paths in total, denoted by Ψ, from the source node to the destinationnode. The candidate paths, denoted by Θ, are included in all paths fromthe source node to the destination node, namely, Θ⊆Ψ, Ψ={l₁, l₂, . . . ,l_(L) _(s) , . . . , l_(O) _(f) }. In the Eq. (5), y_(l) ^(s) representsthe bandwidth allocated to path l, and g_(e,l) ^(s) represents whetherlink e is used in path l of slice s, l₁ is the first candidate path fromthe source node to the destination node, l₂ is the second candidatepath, l_(L) _(s) is the L_(s) ^(th) candidate path, and l_(O) _(f) isthe O_(f) ^(th) path from the source node to the destination node. Amethod for calculating the candidate path includes: 1) by using aprimal-dual algorithm, starting from any feasible flow on a network witha flow value (also known as an available bandwidth) x≤v, increasing theflow values of links on the network and modifying the potentials ofnodes; and 2) iterating the links and the nodes on the network until aflow that meets a predetermined constraint condition is obtained, toobtain a target candidate path, namely, the bandwidth allocation oflinks, where v represents a flow value requested by user (i.e., arequested data transmission rate). If an initial flow value is greaterthan v, the candidate path can be directly obtained. A link priceρ_(e)(x_(e) ^(s)) is calculated according to the bandwidth of link e,where the link price is a function of link bandwidth, and ρ_(e)(x_(e)^(s)) is used as the link power consumption model. A final cost modelbelonging to link e in slice s is ρ_(e)(x_(e) ^(s)), and

$\sum\limits_{e \in \xi_{s}}{\rho_{e}\left( x_{e}^{s} \right)}$

represents the total cost of links in slice s.

Specifically, for a slice s∈S, the user makes a request, and the networkcontroller calculates L_(s) candidate paths from the source node to thedestination node that meet the user demands. There are O paths in total,denoted by Ψ, from the source node to the destination node, and thecandidate paths, denoted by Θ, are included in all paths from the sourcenode to the destination node, namely, Θ⊆Ψ, Ψ={l₁, l₂, . . . , l_(L) _(s), . . . , l_(O) _(f) }. Then several variables with a value of 0 or 1are defined to describe the link states on the network: for the slices∈S, q_(f,l) ^(s) represents whether a request of a user f is carried onpath l, g_(e,l) ^(s) represents whether link e is used on path l, andh_(i,l) ^(s) represents whether node i is used on path l. When acandidate path requested by user f is obtained, the foregoing variablesmay also be determined accordingly. The request of user f may beexpressed as f=(s_(f), t_(f), r_(f)), where s_(f) represents a sourcenode requested by user f, t_(f) represents a destination node requestedby user f, r_(f) represents the data transmission rate requested by userf. The bandwidth allocation on path l is the bandwidth allocation onpath l from the source node to the destination node in slice s, and thedata transmission rate requested by user f is:

$\begin{matrix}{r_{f} = {{\sum\limits_{l \in \Psi}y_{l}^{s}} = {\sum\limits_{l \in \Theta}{q_{f}^{s}{y_{l}^{s}.}}}}} & (21)\end{matrix}$

The bandwidth calculation formula of link e is Eq. (5), and the linkprice is a function of link bandwidth, which may be expressed asρ_(e)(x_(e) ^(s)).

In order to meet a delay requirement of users, the network controllerfirst allocates a candidate path for each user. A network delay includesa propagation delay on a link, a queuing delay, and a processing delayof function modules on a virtual network. A framework has been proposed,which uses the parallelization capability of a general purpose processorto model a processing delay of virtual network function instances, andsolves the global optimal solution by sequentially searching a set offeasible regions. Therefore, when a delay requirement requested by useris given, the total delay budget of user may be obtained by calculatingthe processing delay on a candidate path and a virtual network function,thereby ensuring the delay requirement of user. Next, the selection ofnetwork candidate paths (links) is introduced.

Different users have different requirements for network delay. In orderto ensure the delay requirement of a network slice, the controllercalculates a candidate path for each user that meets the delay budgetrequirement thereof. The network slice relates to a technology fordividing a physical network into a plurality of virtual logical networksthrough virtualizations, and modeling the business request of each user,to form a single-source single-sink minimum cost flow problem. For agiven network N=(s, t, V, A, C, U), where s represents a starting point,t represents an end point, V is a set of network nodes, A is a set oflinks on the network, C is a unit cost, U is an upper bound of flowamounts of links on the network, and 0 is a lower bound of flow amountsof links, a minimum cost flow x with a flow value of v from the sourcenode to the destination node is calculated. For this single-sourcesingle-sink network, when a network capacity meets the requestedconditions, a transit node keeps a flow conservation and a flow with aflow value less than v is referred to as a feasible flow. A residualnetwork corresponding to the feasible flow x on network N may beexpressed as N(x)=(s,t,A(x),C(x),U(x)),

$\begin{matrix}{\left. {{\left. {{{{{A(x)} = {\left\{ {\left( {i,j} \right){❘{{\left( {i,j} \right) \in A},{x_{ij} < u_{ij}}}}} \right\}\bigcup\left\{ \left( {i,j} \right) \right.}}❘}j},\ i} \right) \in A},{x_{ji} > 0}} \right\},} & (22)\end{matrix}$ $\begin{matrix}{{c_{ij}(x)} = \left\{ {\begin{matrix}{c_{ij},} & {{\left( {i,j} \right) \in A},\ {x_{ij} < u_{ij}}} \\{{- c_{ji}},} & {{\left( {j,\ i} \right) \in A},\ {x_{ji} > 0}}\end{matrix},\begin{matrix}\  \\\ \end{matrix}} \right.} & (23)\end{matrix}$ $\begin{matrix}{{u_{ij}(x)} = \left\{ {\begin{matrix}{{u_{ij} - x_{ij}},} & {{\left( {i,j} \right) \in A},\ {x_{ij} < u_{ij}}} \\{x_{ij},} & {{\left( {j,\ i} \right) \in A},\ {x_{ji} > 0}}\end{matrix},} \right.} & (24)\end{matrix}$

where c_(ij)(x) represents a unit cost per flow on the link between nodei and node j through which a flow with a flow value x passes, x_(ij)represents a flow amount on the link between node i and node j throughwhich the flow passes, and u_(ij) represents the upper bound of flowamount on the link between node i and node j. The nodes i and jrepresent adjacent nodes.

Therefore, the minimum cost flow problem with a flow value v from sourcenode s to destination node t may be expressed as follows:

$\begin{matrix}{{\min{\sum\limits_{{({i,j})} \in A}{c_{ij}x_{ij}}}},} & (25)\end{matrix}$${{s.t.}:{{\sum\limits_{{({j,i})} \in A}x_{ji}} - {\sum\limits_{{({j,i})} \in A}x_{ji}}}} = \left\{ {\begin{matrix}{v,} & {i = s} \\{{- v},} & {i = t} \\{0,} & {{i \in V},{i \neq s},t}\end{matrix},} \right.$ 0 ≤ x_(ij) ≤ u_(ij), (i, j) ∈ A.

Eq (25) is to multiply the unit cost per flow by the flow amount, andthen the products are summed up to obtain the total cost.

Using a primal-dual algorithm and introducing dual variables π, z, adual problem of the foregoing problem may be expressed as:

$\begin{matrix}{{w\left( {\pi,z} \right)} = {{\sum\limits_{i \in V}{d_{i}\pi_{i}}} - {\sum\limits_{{({i,j})} \in A}{u_{ij}z_{ij}}}}} & (26)\end{matrix}$ s.t.  : π_(i) − π_(j)  − z_(ij) ≤ c_(ij), (i, j) ∈ A,z_(ij) = 0, (i, j) ∈ A.

A feasible solution to the foregoing problem meets complementary theslackness conditions, i.e.:

x _(ij)(π_(i)−π_(j) −z _(ij) −c _(ij))=0,(i,j)∈A,  (27)

z _(ij)(x _(ij) −u _(ij))=0,(i,j)∈A,  (28)

where π_(i) represents the potential of node i. A core idea of theprimal-dual algorithm is to start from any feasible flow on a networkwith a flow value x≤v, increase the flow values of the links on thenetwork and modify the potentials of nodes, and iterate the links andthe nodes on the network until a flow that meets the constraintcondition is obtained. Generally, the primal-dual algorithm is toaugment the flow amount along the direction of link, π_(i)−π_(j)=c_(ij),and a subnet N⁰(x) including the links meeting π_(i)−π_(j)=c_(ij) in theresidual network N(x) is calculated to find an augmented link in thesubnet for augmentation. The N⁰(x) may be expressed as:

$\begin{matrix}{{N^{0}(x)} = \left\{ {\begin{matrix}{\left( {i,j} \right),{\ }{{{if}\left( {i,j} \right)} \in A},\ {{\pi_{i} - \pi_{j}}\  = c_{ij}},\ {x_{ij} \leq u_{ij}}} \\{\left( {j,i} \right),\ {{{if}{\ }\left( {i,j} \right)} \in A},\ {{\pi_{i} - \pi_{j}}\  = c_{ij}},\ {x_{ij} > 0}}\end{matrix}.} \right.} & (29)\end{matrix}$

When an augmented link with a flow value less than v cannot be found inthe subnet N⁰(x), the potential on node is modified so that the modifiedpotential can meet the foregoing constraints. The algorithm steps may besummarized as follows.

In step 1, a feasible flow x with a source points and an end point t isselected in a network N=(s, t, V, A, C, U), and the initial potential ofeach node is set to π=0.

In step 2, when the flow amount of a feasible flow x≥v, the algorithmends, and a minimum cost flow x with a required flow value v is found ina network; when the flow amount of a feasible flow x<v, in the residualnetwork N(x), the link cost c_(ij)π=c_(ij)−π_(i)+π_(j) is updated, theshortest path length d(i) from source node to node i is calculated, andπ_(i)=π_(i)−d(i) is updated.

In step 3, the maximum flow from node s to node tin the residual networkN⁰(x) is recalculated. If the maximum flow is 0, the problem has nosolution. Otherwise, an augmentation is performed along the links in themaximum flow calculated above, and the operation proceeds to step 1.

The time complexity of the algorithm is analyzed, and it can be seenfrom the foregoing algorithm steps that a loop iteration process is tomodify the flow values of the links in the network and the potentials ofthe nodes with a total number of iterations less than or equal tomin(nU,nC). And the time complexity for calculating the shortest path inthe network is S(n,m,C), and the time complexity for calculating themaximum flow is M(n,m,U), and therefore, the time complexity of thealgorithm may be expressed as O(min{nU,nC}[S(n,m,nC)+M(n,m,U)]).

In the implementation, determining a revenue of the VInP according tothe VInP utility model, the node power consumption model, and the linkpower consumption model in step S4 includes:

determining the utility obtained by the VInP for slice s:

$\begin{matrix}{{{Q_{p}^{s}\left( {x^{s},p^{s}} \right)} = {{{\phi_{s}\left( {x^{s},p^{s},\rho} \right)}r} - {\left( {{\sum\limits_{l \in \Theta}{{\varphi_{i}\left( p_{i}^{s} \right)}h_{i,t}^{s}}} + {\sum\limits_{l \in \Theta}{{\varphi_{e}\left( x_{e}^{s} \right)}g_{e,l}^{s}}}} \right)r}}},} & (6)\end{matrix}$

where ϕ_(s)(x^(s),p^(s),ρ)r represents a charge for the slice providedby the VInP, x^(s) is the bandwidth of slice s, p^(s) is the powerconsumption of slice s, ρ is a price,

$\left( {{\sum\limits_{l \in \Theta}{{\varphi_{i}\left( p_{i}^{s} \right)}h_{i,l}^{s}}} + {\sum\limits_{l \in \Theta}{{\varphi_{e}\left( x_{e}^{s} \right)}g_{e,l}^{s}}}} \right)r$

represents the cost for providing a service, φ_(i)(p_(i) ^(s))represents the price of node i, φ_(e)(x_(e) ^(s)) represents the priceof link e, φ(•) is a monotonical increasing function, h_(i,l) ^(s)represents whether node i is used in path l, and g_(e,l) ^(s) representswhether link e is used in path l;

determining the revenue of the VInP according to the utility obtained bythe VInP for slice s:

$\begin{matrix}{{Q_{p} = {\sum\limits_{s \in S}{Q_{p}^{s}\left( {x^{s},p^{s}} \right)}}},} & (7)\end{matrix}$

where a server has the largest profit, namely, the following conditionsare met:

max Q _(p) ^(s)(x ^(s) ,p ^(s)),

s.t.:x _(e) ^(s) ≤c _(e) ^(pro),

p _(i) ^(s) ≤v _(i) ^(pro),

where x_(e) ^(s) represents the bandwidth of link e, c_(e) ^(pro)represents the remaining maximum bandwidth available for allocation thatis provided by link e, p_(i) ^(s) represents the power consumption ofnode i in slice s, and v_(i) ^(pro) represents the remaining maximumdata transmission rate that node i is able to support.

Specifically, by modeling node energy consumption and links, a networkslice is a virtual network formed by the nodes and the links. The VInPis regarded as the owner of the slice, a user is regarded as a bidder ofthe slice, and the utility model of VInP and the utility model of userneed to be established based on the node and link information,respectively.

The revenue function of VInP may be expressed as a difference between acharge for providing a slice and a cost for providing services on anetwork, and the utility of VInP obtained for a slice may be expressedas Eq. (6). Variables c_(e) ^(pro) and v_(i) ^(pro) are related to thephysical infrastructure and have been determined when the network isestablished.

In the implementation, the revenue of user determined by the userutility model, the node power consumption model, and the link powerconsumption model in step S4 includes:

determining the utility of user:

U _(s)(r)=w _(s) log(l+r),  (8)

where w_(s) represents a service quality request level of user, and rrepresents a data transmission rate; Eq. (8) is used to express therelationship between data transmission rate and user utility;

determining the cost of user for building a slice, according to the nodepower consumption model and the link power consumption model:

$\begin{matrix}{{{\phi_{s}\left( {x^{s},p^{s},\rho} \right)} = {{\sum\limits_{e \in \xi_{s}}{\rho_{e}\left( x_{e}^{s} \right)}} + {\sum\limits_{i \in N_{s}}{\rho_{i}\left( x_{i}^{s} \right)}}}},} & (9)\end{matrix}$

where x_(e) ^(s) represents link allocation, p_(l) ^(s) represents anode allocation status, ρ_(e)(⋅) represents the functional relationshipbetween a link unit price and a link allocation bandwidth, ρ_(i)(⋅)represents the relationship between node unit price and node powerconsumption,

$\sum\limits_{i \in N_{s}}{\rho_{i}\left( x_{i}^{s} \right)}$

represents the cost paid by user for purchasing a node,

$\sum\limits_{e \in \xi_{s}}{\rho_{e}\left( x_{e}^{s} \right)}$

represents the cost paid by user for purchasing a link; ξ_(s) is a setof links in slice s, and N_(s) is a set of nodes in slice s; and

determining the revenue of user according to the utility of user and thecost of user for building a slice, namely, the total revenues from allslices purchased by user:

Q _(c)=Σ_(s∈S) Q _(c) ^(s),  (10)

where the expression above is maximized:

${{\max Q_{c}^{s}} = {{\sum\limits_{f \in K_{s}}{U_{s}\left( r_{f} \right)}} - {{\phi_{s}\left( {x^{s},p^{s},\rho} \right)}r}}},{{{s.t.:}x_{e}^{s}} \geq c_{e}^{req}},{p_{i}^{s} \geq {v_{i}^{req}.}}$

Q_(c) ^(s) represents a revenue from slice s purchased by user, Srepresents a set of slices, r_(f) represents the data transmission raterequested by user f, the expression following s.t. represents aconstraint condition, x_(e) ^(s) represents the bandwidth of link e,C_(e) ^(req) represents the bandwidth requested by user, vi representsthe power consumption of node i in slice s, and V_(i) ^(req) representsthe node data transmission rate requested by user.

Specifically, by modeling node energy consumption and links, a networkslice is a virtual network formed by the nodes and the links. The VInPis regarded as the owner of the slice, a user is regarded as a bidder ofthe slice, and the utility model of VInP and the utility model of userneed to be established based on node information and link information,respectively.

The utility of user mainly includes two parts, namely, a utility fromreceiving data services and a cost paid for occupying resources.Specifically, as shown in Eq. (8), where w_(s) represents a servicequality request level of a user, namely, the requirement for atransmission rate. The higher the requirement for transmission rate ofthe user is, the greater the generated revenue is, and r represents thedata transmission rate. The cost that the a user needs to pay forbuilding slices is a sum of the cost for purchasing nodes and the costfor purchasing links, which may be expressed as Eq. (9).

It should be noted that a slice is provided by network controller. Asthe upper and lower entities on networks, both VInP and users hope tobuild a slice to maximize the utilities thereof. Building a slice meansa revenue or expenditure for a user to purchase the slice.

In the embodiment, step S5 specifically includes:

calculating the total revenue of a slice according to the revenue ofVInP and the revenue of user:

$\begin{matrix}{{{\max Q^{s}} = {{\max\left( {Q_{c}^{s} + Q_{p}^{s}} \right)} = {\max\left( {{\sum\limits_{f \in K_{s}}{w_{s}{\log\left( {1 + {\sum\limits_{l \in \Theta}{q_{f,l}^{s}y_{l}^{s}}}} \right)}}} - {\left( {{\sum\limits_{l \in \Theta}\varphi_{i}},{{\left( p_{i}^{s} \right)h_{i,j}^{s}}\  + {\sum\limits_{l \in \Theta}{{\varphi_{e}\left( x_{e}^{s} \right)}g_{e,l}^{s}}}}} \right)r}} \right)}}},{{{s.t.:}x_{e}^{s}} \geq c_{e}^{req}},{p_{i}^{s} \geq v_{i}^{req}},{x_{e}^{s} \leq c_{e}^{pro}},{p_{i}^{s} \leq v_{i}^{pro}},} & (11)\end{matrix}$

where Q_(c) ^(s) represents the revenue from slice s purchased by user,Q_(p) ^(s) is the utility obtained by VInP from slice s, f is a user,K_(s) is a set of users, w_(s) represents the service quality requestlevel of user, q_(f,l) ^(s) represents whether the request of user f iscarried on path l, y_(l) ^(s) is the bandwidth allocation on path l, Θis the candidate paths included in all paths from the source node to thedestination node, l is a path, φ_(i)(p_(i) ^(s)) represents the price ofnode i, h_(i,l) ^(s) represents whether node i is used on path l,φ_(e)(x_(e) ^(s)) represents the price of link e, g_(e,i) ^(s)represents whether link e is used on path l, c_(e) ^(req) represents thebandwidth requested by user, v_(i) ^(req) represents the node datatransmission rate requested by user, C_(e) ^(pro) represents theremaining maximum bandwidth available for allocation that is provided bylink, V_(i) ^(pro) represents the remaining maximum data transmissionrate that is provided and is able to be supported by node i, and theconstraint condition is the constraint on requesting for a node and alink by user, and the constraint on responding to a node and bandwidthat the VInP side.

Specifically, in order to maximize the overall benefit, a VInP providesslice resources for a plurality of users. A user builds a utilityfunction thereof to evaluate a slice. Different users have differentestimated values for a same slice, and through a competitive game to theslice, the slice resource is allocated.

The construction of network model is based on the allocation of networkslice resources. For the foregoing establishment of problem model, thelink price is related to the number of links occupied by a slice, andthe node price is also related to the node resource occupied by a slice.For user f slice s is allocated, and the generated data flow istransmitted at rates of r₁, r₂, . . . , r_(k) on a plurality of pathsl₁, l₂, . . . , l_(k). If the price of each link and the price of eachnode are fixed, the unit price ρ(l) for path l in a slice may be definedas a sum of link resources and node resources through which a unit ofdata is transmitted, and is expressed as ρ(l)=ρ_(e)(x_(e) ^(s))+ρ(p_(i)^(s)). The prices for the plurality of paths l₁, l₂, . . . , l_(k) maybe sorted as ρ(l₁)≤ρ(l₂)≤ . . . ≤ρ(l_(k)). Assumed that the total datatransmission rate for user f is t, then t may be expressed as:

$\begin{matrix}{t = {\sum\limits_{i = 1}^{k}{r_{i}.}}} & (30)\end{matrix}$

Therefore, the objective function of user may be expressed as:

$\begin{matrix}{{Q_{c}^{s} = {{{\sum\limits_{f \in K_{s}}{U_{s}(t)}} - {{\rho(l)}t}} = {{w_{s}{\log\left( {1 + t} \right)}} - {{\rho(l)}t}}}}.} & (31)\end{matrix}$

In order to maximize the objective function, the optimal datatransmission rate of user f may be expressed as:

$\begin{matrix}{t^{\star} = {\frac{w_{s}}{\rho(l)} - 1.}} & (32)\end{matrix}$

If the resource allocation of slices is unchanged and the transmissionis always performed on a path with the lowest price, a waste ofresources will be caused. Although various resources in slice s have thelowest unit price, the final benefit is not necessarily the largest. Inthe present disclosure, the link resources and node resources in a sliceare auctioned by using an auction algorithm, and a plurality of userscompete for the slice resources, so as to maximize their benefits. Theusers need to meet their demands while accessing networks at the minimumcost. The VInP provides users with the customized slice services whileimproving the economic benefits of networks. On this issue, we formulatethe problem of slice resource allocation as an auction problem. Wedetermine the allocation strategies of link resources and node resourcesaccording to the prices submitted by a plurality of users, namely,bidding information, so as to maximize the overall benefit of thesystem.

In the embodiment, when a competitive game mechanism is used to allocatethe slice resources in step S6, a mutual information-based competitivegame strategy is adopted. The revenue at the user end is used as theestimated value of user for a slice, the revenue at the VInP end is usedas the value of slice. The estimated value a of user for a slice is arandom variable, the value of slice is also a random variable p, thereis a specific relationship between a and p. I(a,p) represents a degreeof correlation between the estimated value of slice and the value ofslice.

The following specifically introduces the competitive game allocationstrategy adopted in the present disclosure in the process of solving thenetwork model.

For the allocation of slice resources, a plurality of users make theirrequests to a VInP, and the VInP may use an auction algorithm to realizea competitive game for a slice among a plurality of users. The slicevalue has a specific relationship with the estimated value of user forthe slice. The mutual information may be used to represent therelationship between the slice value and the estimated value, and thisrelationship is fully considered in the competitive game process, forthe auction of slice resources.

The model frame diagram of an auction system in the competitive gameprocess is illustrated in FIG. 2 , in which different users as bidderssubmit their requests to the VInP. The bidding prices of different noderesources and link resources are different, and the objective of usersis to meet their performance requirements with a lowest price, while aservice provider hopes to maximize its benefits by auctioning networkresources. Users determine their bidding prices through competitive gamestrategies, namely, Eq. (40). Finally, an auctioneer determines thefinal ownership of slice resources.

(1) The Game Strategy Based on Mutual Information

The slice value has a specific relationship with the estimated value ofuser for this slice. The mutual information may be used to express therelationship between the slice value and the estimated value, so as toput forward a game strategy based on mutual information. As a measure ofsimilarity, the mutual information can describe the correlation degreebetween two variables. The greater a value of mutual information betweentwo variables is, the greater the correlation degree is. The mutualinformation coming from the information theory, is used to describe astatistical correlation degree between two variables, and is usuallyrepresented by information entropy. The information entropy is firstproposed by Shannon to measure how much information a variable includes.It is assumed that P(X) represents a probability that an event X occurs.The definition of information entropy is:

$\begin{matrix}{{H\left( {X,Y} \right)} = {- {\sum\limits_{x}{{P(x)}\log{{P(x)}.}}}}} & (33)\end{matrix}$

The information entropy is a statistic that measures the uncertainty ofa variable. The greater the uncertainty is, the greater thecorresponding information entropy is. Therefore, when variables occurwith a same probability, the information entropy reaches a maximumvalue. When a variable occurs with a probability of 1, the informationentropy reaches the minimum value of 0. Joint entropy is the measure ofuncertainty degree to a jointly distributed random system, and is anamount of information obtained by observing a random system with one ormore random variables. The joint entropy is defined as:

$\begin{matrix}{{{H\left( {X,Y} \right)} = {- {\sum\limits_{x}{\sum\limits_{y}{{P\left( {x,y} \right)}\log{P\left( {x,y} \right)}}}}}},} & (34)\end{matrix}$

where x and y are the specific values of X and Y correspondingly, P(x,y)is the joint probability of these values appearing together. The mutualinformation indicates whether two variables are related to each other,and the correlation strength. The greater the mutual information is, thestronger the correlation between variables is. The mutual informationmay be regarded as an extension of correlation coefficients in the caseof high-dimensional nonlinearity. It is assumed that H(A) and H(B) areinformation entropy of user A and user B respectively, and H(A,B)represents the joint entropy of two users. The mutual information isdefined as: I(A,B)=H(A)+H(B)—H(A,B). The mutual information describes adegree of statistical independence between two users. If A and B areindependent of each other, I(A,B)=0. If A and B are completely dependentor completely inclusive, H(A)=H(B)=H(A,B), and the mutual information isthe largest at this time.

In the game of the present disclosure, the estimated value a of user fora slice is used as a random variable, a slice value is also a randomvariable p, there is a specific relationship between a and p. I(a,p)represents the correlation degree between the estimated value and theslice value.

Different users submit their requests to a VInP as bidders, and thebidding prices of different node resources and link resources aredifferent. The objective of users is to meet their performancerequirements with a lowest price, while a service provider hopes tomaximize its benefits by auctioning network resources. It is assumedthat the network benefit obtained by the k^(th) user, to whom the l^(th)network slice is allocated, is a_(kl). For an auction system, tomaximize the overall network benefits of system, a set of assignablenetwork slices for the k^(th) service flow is A(k). If A(k)=φ, itindicates that the request is unreachable and the access is denied;otherwise, the optimal allocation scheme is selected for the k^(th) userin set A(k) according to the bidding information of users. The set B isused to represent a two-tuple consisting of the k^(th) user and thel^(th) network slice, namely:

B={(k,l)|l∈A(k),k=1,2, . . . ,n}.  (35)

The set S of bidding information is a set of two-tuples consisting ofthe k^(th) user and the l^(th) slice. If the set is empty, the sliceneeds to be recreated. Otherwise, a customized network slice isauctioned. The set S should satisfy the following items:

1) for ∀(k,l)∈S, l∈A(k);

2) for each service flow k from user, there exists (∃) at most one group(k, l) pertaining to set S, and for each slice l, there exists (∃) atmost one group (k, l) pertaining to set S.

The network resources are auctioned by using the auction algorithm whilemeeting ε complementary slackness conditions, namely, if a value vectorp=(p₁, p₂, . . . , p_(n)) of slice l and the allocation of S satisfies:

$\begin{matrix}{{{a_{kl} - p_{l}} \geq {{\max\limits_{t \in {A(k)}}\left( {a_{kl} - p_{t}} \right)} - \varepsilon}},} & (36)\end{matrix}$

then when the l^(th) slice is allocated to the k^(th) user, the networkbenefit is the most. If complementary slackness conditions ε, aresatisfied for each slice, it refers to that S and P satisfy the εcomplementary slackness conditions. During the auction process, theslice price may not drop, and the users to whom slices have beenallocated upon the beginning of iteration are still in the state ofbeing allocated at the end. The auction process is mainly divided intoan auction bidding stage and an auction allocation stage.

(2) Auction Bidding Stage

The k^(th) user loops through all network slices in A(k) to calculatethe optimal network slice l_(k) with a subscript of k, namely:

$\begin{matrix}{{l_{k} = {\arg\max\limits_{l \in {A(k)}}\left\{ {a_{kl} - p_{l}} \right\}}},} & (37)\end{matrix}$

for the k^(th) user, an expected revenue from bidding is v_(k), whichmay be expressed as:

$\begin{matrix}{{v_{k} = {\max\limits_{l \in {A(k)}}\left\{ {a_{k,l} - p_{l}} \right\}}},} & (38)\end{matrix}$

other network slices other than l_(k) are found to maximize the revenueof the k^(th) user, expressed as w_(k), namely:

$\begin{matrix}{{w_{k} = {\max\limits_{{l \in {A(k)}},{l \neq l_{k}}}\left\{ {a_{kl} - p_{l}} \right\}}},} & (39)\end{matrix}$

where w_(k) represents the second largest benefit value.

If set A(k) includes only one slice l, w_(k)=−∞, and a bid of the k^(th)user for the l_(k) ^(th) slice may be expressed as:

b _(kl) _(k) =p _(l) _(k) +v _(k) −w _(k) +εI(a _(kl) _(k) ,p _(l))=a_(kl) _(k) −w _(k) +εI(a _(kl) _(k) ,p _(l)).  (40)

Bid adjustment of the k^(th) user for slice l is related to mutualinformation. When the estimated slice value of user is similar to theslice value, namely, the correlation is stronger, the user may adjustthe price appropriately. If the correlation between the estimated slicevalue of user and slice value is weaker, it indicates that the requestedslice is not the slice excepted by user, and the bidding price may notchange much.

(3) Auction Allocation Stage

One network slice may meet the network performance requirements of aplurality of service flows, therefore, it is needed to select theoptimal one from the bidding information for allocation. A set ofbidding information for the l^(th) slice is represented by P_(I). Thehighest bid p_(I) is selected to update set S, and replace (k,l) with(k_(l),l),

$\begin{matrix}{{p_{l} = {\max\limits_{k \in {P(l)}}b_{kl}}}.} & (41)\end{matrix}$

Generally, there are two options for bidding users. One is that a bidincludes only one unassigned user, which is similar to the Gauss-Seidelmethod for solving nonlinear equations and is suitable for calculationin a serial environment. The other one is that a bid includes allunassigned users, which is similar to the Jacobi method for solvingnonlinear equations and is suitable for calculation in a parallelenvironment. In the iterative process of bidding, the bidding price isincreased by at least E each time, namely:

b _(kl) _(k) =p _(l) _(k) +εI(a _(kl) _(k) ,p _(l)).  (42)

At the end of each iteration, new allocation is made to network slices,so that a slice that receives a bid is allocated to a service flow thatis not allocated at the beginning. In the process of auction, the εcomplementary slackness conditions can be kept satisfied, namely, theallocation and value vectors satisfy the ε complementary slacknessconditions at the beginning, and the ε complementary slacknessconditions are still satisfied when the iteration ends. Each bidding ofservice flow may make the expected revenue v_(k) of service flow bereduced by ε or remain unchanged. When the expected revenue remainsunchanged, it indicates that there are at least two slices, so that thebusiness flow revenue is v_(k), where

$v_{k} = {\max\limits_{l \in {A(k)}}{\left\{ {a_{kl}\  - p_{l}} \right\}.}}$

The l_(k) ^(th) network slice that accepts the bid has its bidding priceincreased by at least c, therefore, the l_(k) ^(th) slice cannot beallocated to the k^(th) service flow, and a re-bidding is required.

The scale of ε in bidding algorithm has a great influence on a therunning time of this algorithm. Generally, a larger ε value is selectedat the beginning, and with the iteration of bidding information, εgradually decreases. The final ε should make nε small enough to use thefollowing equation to iterate:

$\begin{matrix}{{\varepsilon^{k + 1} = \frac{\varepsilon^{k}}{\theta}},{k = 0},1,\ldots,} & (43)\end{matrix}$

where ε⁰ is an initial value of ε, and θ is an integer greater than 1.

When the auction algorithm has no a feasible solution, the system doesnot know whether the problem is unsolvable or is difficult to be solved,unless the conditions are further restricted. Therefore, some specificconstraints are required. The unsolvable judgment basis for the auctionalgorithm is that v_(k) has a lower bound at the beginning, and thefinal iteration makes v_(k) be not lower than the lower bound.

In the following, a specific example is used to compare the methodprovided in the present disclosure to the existing method to verify theresource allocation effect of the present disclosure. The scheme (CC)proposed in the present disclosure is compared with a Stackelberg Game(SG) strategy and a Dynamic Allocation (DA) strategy.

As shown in FIG. 3 , in the initial stage, for a node, because aleader-followers mode is used in Stackelberg Game to allocate resources,the node utilization rate is high. The resources are allocateddynamically according to user requests in Dynamic Allocation (DA)strategy, therefore, the node utilization rate is relatively high.However, as the number of iterations increases, SG does not consider asituation in which multiple users competes for resources, and there isno master/subordinate relationship between multiple users, which maylead to a relatively low node utilization rate. Best effort is made tomeet the demands of users in DA strategy, so that the node utilizationrate is relatively low. The relationship among users for resourcecompetition is fully considered in CC, and the mutual information isused to associate with the relationship between users and slices, sothat the node utilization rate is improved.

As shown in FIG. 4 , the utilization rates of link in three differentschemes increase as the number of iterations increases. For the DAresource allocation scheme, the price of link resources does not change,and network controller preferentially allocates the cheap links. As thenumber of iterations increases, the network capacity of a key linkquickly reaches the limit, making the utilization rate of link resourcesbe maximized. However, for CC and SG, the user and the VInP are in agame. The network controller dynamically allocates the link resourcesaccording to the game strategy, and the link price is dynamicallyadjusted according to the network status to avoid the emergence of keylink, so that the utilization rate of link is improved. A link ismodeled in CC, and the link price is related to the link allocationbandwidth, so that the utilization rate of link is improved.

As shown in FIG. 5 , the three slice resource schemes are compared.Regarding the revenue of VInP, there is a little difference among thethree slice resource schemes in the initial iteration. As the number ofiterations increases, an advantage of resource allocation scheme basedon a game theory is reflected, and the revenues of VInP in CC and SG aregreater than the revenue of VInP in DA. Because mutual informationtheory is used in CC to associate the slice value with the estimatedslice value of user and adjust the competitive game strategy, theutility of VInP reaches to the maximum value earlier.

In general, aiming at a multi-user slice resource allocation method, thepresent disclosure formulates the slice resource allocation as atwo-tier architecture of virtual infrastructure service providers(VInPs) and users. The VInPs and users establish their utility modelsrespectively. A marginal economic benefit is used to build a revenuemodel for user, and the user satisfaction is modeled by considering therelationship between the resources requested by user and the actualallocated resources. A cooperative competition mechanism is used toadjust a resource price and a quantity of allocated resources, such thata Nash equilibrium is finally reached. Compared to other gamestrategies, the user utility has been improved; and the experiments areused to verify the existence of Nash equilibrium. For a situation wherea plurality of users request a same network slice resource, the nodeenergy consumption and link selection play an important role in sliceresource allocation. A slice is modeled as a combination of nodes andlinks, a plurality of users bid on this slice, and the competitive gamestrategy is used to allocate slice resources to the users, and theestimated slice value of user is the utility function. It is proposed touse mutual information to reflect the relationship between an estimatedslice value and a slice value, and a slice resource allocation scheme isobtained by adjusting a bidding strategy. Finally, the utilization rateof node and link resources in this scheme is proved to be improvedthrough experiments, and the introduction of mutual information enablesthe system to reach a Nash equilibrium earlier and improves the networkbenefits.

The above embodiments are provided merely for an objective of describingthe present disclosure and are not intended to limit the scope of thepresent disclosure. The scope of the present disclosure is defined bythe appended claims. Various equivalent replacements and modificationsmade without departing from the spirit and scope of the presentdisclosure should all fall within the scope of the present disclosure.

1: A multi-user slice resource allocation method based on competitivegame, comprising: S1: modeling a system as a two-tier architecture ofvirtual infrastructure service providers (VInPs) and users, wherein aVInP layer comprises a plurality of VInPs and a user layer comprises aplurality of users; S2: building a VInP utility model and a user utilitymodel; S3: dividing a slice resource into nodes and links forallocation, and building a node power consumption model and a link powerconsumption model; S4: determining a revenue of each VInP according tothe VInP utility model, the node power consumption model, and the linkpower consumption model, and determining a revenue of each useraccording to the user utility model, the node power consumption model,and the link power consumption model; S5: calculating a total revenue ofa slice according to the revenue of the VInP and the revenue of theuser, and using the total revenue of the slice as a network model; andS6: solving the network model, wherein the VInP is used as a seller, theuser is used as a buyer, the seller determines an initial priceaccording to a total quantity of slice resources, and the buyer bids onthe slice; and allocating the slice resources by using a competitivegame mechanism. 2: The allocation method according to claim 1, whereinthe VInP utility model in S2 is built as:G(p,q)=pq−cq,  (1) wherein p represents an initial unit price of theslice resources given by the VInP, q represents a quantity of sliceresources allocated by the VInP, and c represents a cost unit price ofthe slice resources; the user utility model is:F(p,q)=u(q)−l(p,q)+v(q),  (2) wherein u(q) represents a utilitygenerated from the slice resources acquired by user, l(p,q) represents acost expended by the user for the resource, v(q) represents the usersatisfaction, u(q)=wln(1+q), l(p, q)=pq,${{v(q)} = {\ln\left( \frac{m + q}{m} \right)}},$  wherein w is aconstant greater than 0 and represents a user weight, m represents aquantity of resources requested by the user. 3: The allocation methodaccording to claim 1, wherein the building a node power consumptionmodel in S3 specifically comprises: calculating power consumption of asingle node:P _(i) =P _(i) ^(SE) +P _(i) ^(RE),  (3) wherein P_(i) represents linkpower consumption of a accessed node i in a slice, P_(i) ^(SE)represents transmission power consumption of the accessed node i, andP_(i) ^(RE) represents reception power consumption of the accessed nodei; calculating node power consumption of the slice according to powerconsumption of the single node:p _(i) ^(s) =Σh _(i,l) ^(s) p _(i),  (4) wherein h_(i,l) ^(s) representswhether the node I is used in a path l, the path represents a completelink from a source node to a destination node, and s represents a labelof the slice; and determining a node price ρ_(i)(p_(i) ^(s)) accordingto the node power consumption of the slice, wherein the node price is afunction of the node power consumption, and ρ_(i)(p_(i) ^(s)) is used asthe node power consumption model. 4: The allocation method according toclaim 3, wherein the building a link power consumption model in S3specifically comprises: calculating a bandwidth of a link e:$\begin{matrix}{{x_{e}^{s} = {{\sum\limits_{l \in \Psi}y_{l}^{s}} = {\sum\limits_{l \in \Theta}{g_{e,l}^{s}y_{l}^{s}}}}},} & (5)\end{matrix}$ wherein a network controller calculates L_(s) candidatepaths from the source node to the destination node that meet userdemands, paths from the source node to the destination node are denotedby Ψ and amount to O paths in total, the candidate paths, denoted by Θ,are comprised in all paths from the source node to the destination node,namely, Θ⊆Ψ, Ψ={l₁, l₂, . . . , l_(L), . . . , l_(O) _(f) }, y_(l) ^(s)represents bandwidth allocation on a path l, and g_(e,l) ^(s) representswhether a link e is used in the path l of a slice s, l₁ is a firstcandidate path from the source node to the destination node, l₂ is asecond candidate path, l_(L) _(s) is an L_(s) ^(th) candidate path, andl_(O) _(f) is an O_(f) ^(th) path from the source node to thedestination node, wherein a method for calculating the candidate pathscomprises: starting, through using a primal-dual algorithm, from anyfeasible flow in a network with a flow value x≤v, increasing the flowvalues of links in the network and modify potentials of nodes; anditerating the links and the nodes in the network until a flow that meetsa predetermined constraint condition is obtained, to obtain a targetcandidate path, namely, bandwidth allocation of the link, wherein vrepresents a flow value requested by the user, namely, a requested datatransmission rate, wherein if an initial flow value is greater than v,the target candidate path is directly obtained; and a link priceρ_(e)(x_(e) ^(s)) is calculated according to the bandwidth of the linke, wherein the link price is a function of the link bandwidth,ρ_(e)(x_(e) ^(s)) is used as the link power consumption model, and x_(e)^(s) represents the bandwidth of the link e. 5: The allocation methodaccording to claim 1, wherein the determining a revenue of each VInPaccording to the VInP utility model, the node power consumption model,and the link power consumption model in S4 comprises: determiningutility obtained by the VInP from a slice s: $\begin{matrix}{{{Q_{p}^{s}\left( {x^{s},p^{s}} \right)} = {{{\phi_{s}\left( {x^{s},p^{s},\rho} \right)}r} - {\left( {{\sum\limits_{l \in \Theta}{{\varphi_{i}\left( p_{i}^{s} \right)}h_{i,l}^{s}}} + {\sum\limits_{l \in \Theta}{{\varphi_{e}\left( x_{e}^{s} \right)}g_{e,l}^{s}}}} \right)r}}},} & (6)\end{matrix}$ wherein ϕ_(s)(x^(s),p^(s),ρ)r represents a charge for theslice s provided by the VInP, x^(s) is a bandwidth of the slice s, p^(s)is power consumption of the slice s, ρ is a price,$\left( {{\sum\limits_{l \in \Theta}{{\varphi_{i}\left( p_{i}^{s} \right)}h_{i,l}^{s}}} + {\sum\limits_{l \in \Theta}{{\varphi_{e}\left( x_{e}^{s} \right)}g_{e,l}^{s}}}} \right)r$ represents a cost for providing a service, φ_(i)(p_(i) ^(s)) representsa price of a node i, φ_(e)(x_(e) ^(s)) represents a price of a link e,h_(i,l) ^(s) represents whether the node i is used in a path l, g_(e,l)^(s) represents whether the link e is used in a path l, p_(i) ^(s) isnode power consumption of the slice s, and r represents a datatransmission rate; and determining the revenue of the VInP according tothe utility obtained by the VInP for the slice s: $\begin{matrix}{{Q_{p} = {\sum\limits_{s \in S}{Q_{p}^{s}\left( {x^{s},p^{s}} \right)}}},} & (7)\end{matrix}$ wherein S represents a set of slices, and a server has alargest profit, namely, following conditions are met:max Q _(p) ^(s)(x ^(s) ,p ^(s)),s.t.:x _(e) ^(s) ≤c _(e) ^(pro),p _(i) ^(s) ≤v _(i) ^(pro), wherein x_(e) ^(s) represents a bandwidth ofthe link e, c_(e) ^(pro) represents a remaining maximum bandwidthavailable for allocation that is provided by the link e, p_(i) ^(s)represents power consumption of the node i in the slice s, and v_(i)^(pro) represents a remaining maximum data transmission rate that isprovided and is able to be supported by the node i. 6: The allocationmethod according to claim 1, wherein the determining a revenue of eachuser according to the user utility model, the node power consumptionmodel, and the link power consumption model in S4 comprises: determiningutility of the user:U _(s)(r)=w _(s) log(l+r),  (8) wherein w_(s) represents a servicequality request level of the user, and r represents a data transmissionrate; determining a cost of the user for building the slice according tothe node power consumption model and the link power consumption model:$\begin{matrix}{{{\phi_{s}\left( {x^{s},p^{s},\rho} \right)} = {{\sum\limits_{e \in \xi_{s}}{\rho_{e}\left( x_{e}^{s} \right)}} + {\sum\limits_{i \in N_{s}}{\rho_{i}\left( x_{i}^{s} \right)}}}},} & (9)\end{matrix}$ wherein x_(e) ^(s) represents link allocation, p_(i) ^(s)represents a node allocation status, ρ_(e)(⋅) represents a functionalrelationship between a link unit price and a link allocation bandwidth,ρ_(i)(⋅) represents a relationship between a node unit price and nodepower consumption,$\sum\limits_{e \in \xi_{s}}{\rho_{e}\left( x_{e}^{s} \right)}$ represents a cost paid by the user for purchasing the link,$\sum\limits_{i \in N_{s}}{\rho_{i}\left( x_{i}^{s} \right)}$ represents a cost paid by the user for purchasing the node; ξ_(s) is aset of links of a slice s, and N_(s) is a set of nodes in the slice s;determining the revenue of the user according to the utility of the userand the cost of the user for building the slice, namely, a total revenueof the user for purchasing all slices:Q _(c)=Σ_(s∈S) Q _(c) ^(s),  (10) wherein a following equation ismaximized:${{\max Q_{c}^{s}} = {{\sum\limits_{f \in K_{s}}{U_{s}\left( r_{f} \right)}} - {{\phi_{s}\left( {x^{s},p^{s},\rho} \right)}r}}},$s.t.:x_(e)^(s) ≥ c_(e)^(req), p_(i)^(s) ≥ v_(i)^(req), Q_(c) ^(s)represents a revenue from the slice s purchased by the user, Srepresents a set of slices, r_(f) represents a data transmission raterequested by a user f, a expression following s.t. represents aconstraint condition, x_(e) ^(s) represents a bandwidth of a link e,c_(e) ^(req) represents a bandwidth requested by the user, p_(i) ^(s)represents power consumption of a node i in the slice s, v_(i) ^(req)represents a node data transmission rate requested by the user, andU_(s)(r_(f)) is utility of the user f. 7: The allocation methodaccording to claim 5, wherein S5 specifically comprises: calculating thetotal revenue of the slice according to the revenue of the VInP and therevenue of the user: $\begin{matrix}{\left. {{\max Q^{s}} = {{\max\left( {Q_{c}^{s} + Q_{p}^{s}} \right)} = {{\max{\sum\limits_{f \in K_{s}}{w_{s}{\log\left( {1 + {\sum\limits_{l \in \Theta}{q_{f,l}^{s}y_{l}^{s}}}} \right)}}}} - {\left( {{\sum\limits_{l \in \Theta}{{\phi_{i}\left( p_{i}^{s} \right)}h_{i,j}^{s}}} + {\sum\limits_{l \in \Theta}{{\phi_{e}\left( x_{e}^{s} \right)}g_{e,l}^{s}}}} \right)r}}}} \right),} & (11)\end{matrix}$s.t.:x_(e)^(s) ≥ c_(e)^(req), p_(i)^(s) ≥ v_(i)^(req), x_(e)^(s) ≤ c_(e)^(pro), p_(i)^(s) ≤ v_(i)^(pro),wherein Q_(c) ^(s) represents the revenue from the slice s purchased bythe user, Q_(p) ^(s) is the utility obtained by the VInP for the slices, f is a user, K_(s) is a set of users, w_(s) represents the servicequality request level of the user, q_(f,l) ^(s) represents whether arequest of the user f is carried in the path l, y_(l) ^(s) is bandwidthallocation on the path l, Θ is the candidate paths comprised in all thepaths from the source node to the destination node, l is a path,φ_(i)(p_(i) ^(s)) represents the price of the node i, h_(i,l) ^(s)represents whether the node i is used in the path, φ_(e)(x_(e) ^(s))represents the price of the link e, g_(e,l) ^(s) represents whether thelink e is used in the path l, c_(e) ^(req) represents the bandwidthrequested by the user, V_(i) ^(req) represents the node datatransmission rate requested by the user, c_(e) ^(pro) represents theremaining maximum bandwidth available for allocation that is provided bythe link, v_(i) ^(pro) represents the remaining maximum datatransmission rate that is able to be supported by the node, and theconstraint condition comprises a constraint on the user requesting anode and link, and a constraint on the VInP responding to a node andbandwidth constraint. 8: The allocation method according to claim 1,wherein when the competitive game mechanism is used to allocate theslice resources in S6, a mutual information-based competitive gamestrategy is adopted, and the revenue of the user is used as a estimatedvalue of the user for the slice, the revenue of the VInP is used as aestimated value for the slice, a estimated value a of the user for theslice is used as a random variable, a value of the slice is also arandom variable p, a specific relationship exist between a and p, andI(a,p) represents a degree of correlation between the estimated valuefor the slice and the value of the slice. 9: The allocation methodaccording to claim 6, wherein S5 specifically comprises: calculating thetotal revenue of the slice according to the revenue of the VInP and therevenue of the user: $\begin{matrix}{\left. {{\max Q^{s}} = {{\max\left( {Q_{c}^{s} + Q_{p}^{s}} \right)} = {{\max{\sum\limits_{f \in K_{s}}{w_{s}{\log\left( {1 + {\sum\limits_{l \in \Theta}{q_{f,l}^{s}y_{l}^{s}}}} \right)}}}} - {\left( {{\sum\limits_{l \in \Theta}{{\phi_{i}\left( p_{i}^{s} \right)}h_{i,j}^{s}}} + {\sum\limits_{l \in \Theta}{{\phi_{e}\left( x_{e}^{s} \right)}g_{e,l}^{s}}}} \right)r}}}} \right),} & (11)\end{matrix}$s.t.:x_(e)^(s) ≥ c_(e)^(req), p_(i)^(s) ≥ v_(i)^(req), x_(e)^(s) ≤ c_(e)^(pro), p_(i)^(s) ≤ v_(i)^(pro),wherein Q_(c) ^(s) represents the revenue from the slice s purchased bythe user, Q_(p) ^(s) is the utility obtained by the VInP for the slices, f is a user, K_(s) is a set of users, w_(s) represents the servicequality request level of the user, q_(f,l) ^(s) represents whether arequest of the user f is carried in the path l, y_(l) ^(s) is bandwidthallocation on the path l, Θ is the candidate paths comprised in all thepaths from the source node to the destination node, l is a path,φ_(i)(p_(i) ^(s)) represents the price of the node i, h_(i,l) ^(s)represents whether the node i is used in the path, φ_(e)(x_(e) ^(s))represents the price of the link e, g_(e,l) ^(s) represents whether thelink e is used in the path l, c_(e) ^(req) represents the bandwidthrequested by the user, V_(i) ^(req) represents the node datatransmission rate requested by the user, c_(e) ^(req) represents theremaining maximum bandwidth available for allocation that is provided bythe link, v_(i) ^(pro) represents the remaining maximum datatransmission rate that is able to be supported by the node, and theconstraint condition comprises a constraint on the user requesting anode and link, and a constraint on the VInP responding to a node andbandwidth constraint.